In traditional machine learning approaches, most work on statistical detection of sarcasm has relied on various combinatory forms of Random Forests (RF), Support Vector Machines (SVM), Decision trees (DT), Naïve Bayes (NB) and Neural Networks (NN) [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. For instance, [8]} use SVM with sequential minimal optimization (SMO) and Logistic Regression (LogR), which are usually used toward sentiment analysis, to identify discriminating features. [9]} utilize a hybrid SVM system that outperformed the SVM classifier. Similarly, the use of balanced winnow algorithms to determine high-ranking features [10]}, Naive Bayes and Decision Trees for multiple pairs of labels among irony, humor, politics, and education [11]} and fuzzy clustering for sarcasm detection [12]} are reported. [13]} present the use of binary Logistic Regression and SVM-HMM toward incorporating the sequential nature of output labels into a conversation. Likewise, [2]} report that sequence labeling algorithms are more useful for conversational data as opposed to classification methods. They use SVM-HMM and SEARN as the sequence labeling algorithms. [15]} present a multi-strategy ensemble learning approach (MSELA) including Bagging, Boosting, etc., to handle the imbalance between sarcastic and non-sarcastic samples.
